mi.polr: Elementary function: multinomial log-linear models to impute...
In mi: Missing Data Imputation and Model Checking

Description Usage Arguments Details Value Author(s) References See Also Examples

Imputes missing data in a categorical variable using multinomial Log-linear Models.

  mi.polr(formula, data = NULL, drop.unused.levels = TRUE, start = NULL,
    maxit = 100, missing.index = NULL, ...)
  ## S4 method for signature 'mi.polr'
residuals(object, y)
  ## S4 method for signature 'mi.polr,ANY'
plot( x, y, main=deparse( substitute( y ) ), gray.scale = FALSE, ... )

`formula`	a formula expression as for regression models, of the form `response ~ predictors`. The response should be a factor (preferably an ordered factor), which will be interpreted as an ordinal response, with levels ordered as in the factor. The model must have an intercept: attempts to remove one will lead to a warning and be ignored. An offset may be used. See the documentation of 'formula' for other details.
`data`	A data frame containing the incomplete data and the matrix of the complete predictors.
`drop.unused.levels`	Drops unused levels.
`start`	Starting value for bayespolr.
`maxit`	Maximum number of iteration for bayespolr. The default is 100.
`missing.index`	The index of missing units of the outcome variable.
`...`	Currently not used.
`object`	`mi.polr` object.
`x`	`mi.polr` object.
`y`	Observed values.
`main`	main title of the plot.
`gray.scale`	When set to TRUE, makes the plot into gray scale with predefined color and line type.

multinom calls the library nnet. See multinom for other details.

`model`	A summary of the multinomial fitted model
`expected`	The expected values estimated by the model
`random`	Vector of length n.mis of random predicted values predicted by using the multinomial distribution
`residual`	The residual vector of length same as y

Masanao Yajima yajima@stat.columbia.edu, Yu-Sung Su ys463@stat.columbia.edu, M.Grazia Pittau grazia@stat.columbia.edu, Andrew Gelman gelman@stat.columbia.edu

Yu-Sung Su, Andrew Gelman, Jennifer Hill, Masanao Yajima. (2011). “Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box”. Journal of Statistical Software 45(2).

Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.

mi.info, mi.method, mi

  # true data
  x<-rnorm(100,0,1) # N(0,1)
  y<-(1+2*x)+rnorm(100,0,1)
  y<-round(y)
  y[y<0]<-0
  # create artificial missingness on y
  y[seq(1,100,10)]<-NA
  dat.xy <- data.frame(x,y)
  # imputation
  polr.imp <- mi.polr(y~x, data = dat.xy)